How do I choose the right value of k in k-fold cross-validation?

What factors should I consider when choosing k for k-fold cross-validation?

You should consider the size of your dataset, the model complexity, and the computational resources available.

Is there a general rule of thumb for selecting k?

A common rule of thumb is to set k to the square root of the number of samples, but it can vary based on your specific case.

What happens if k is too small or too large?

If k is too small, you may have high variance in your results. If it's too large, it can lead to high bias and increased computation time.

Can I use cross-validation with imbalanced datasets?

Yes, but you should consider stratified k-fold to ensure each fold has a representative distribution of classes.